import cv2

# Recognizer configuration
alphabets = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J',
            'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
            'U', 'V', 'W', 'X', 'Y', 'Z']

# ANN configuration

nsteps = 10000
step_size = 0.01
max_err = 0.0001
momentum = 0
condition = cv2.TERM_CRITERIA_COUNT | cv2.TERM_CRITERIA_EPS

nInputNodes = 6  # same as number of features
nHiddenNodes1 = 10  # number of nodes in the first hidden layer
nHiddenNodes2 = 10  # number of nodes in the second hidden layer
# nHiddenNodes<i> = <n>  # to add more hidden layers
nOutputs = len(alphabets)  # number of alphabets

layersList = [nInputNodes, nHiddenNodes1, nHiddenNodes2, nOutputs]  # modify this to add more hidden layers